A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

Uwaise Ibna Islam, A.K. Enamul Haque, Dheyaaldin Alsalman, Muhammad Nazrul Islam, Mohammad Ali Moni and Iqbal H. Sarker ()
Additional contact information
Uwaise Ibna Islam: Chittagong University of Engineering & Technology
Dheyaaldin Alsalman: Dar Al-Hekma University
Muhammad Nazrul Islam: Military Institute of Science and Technology
Mohammad Ali Moni: The University of Queensland
Iqbal H. Sarker: Chittagong University of Engineering & Technology

Annals of Data Science, 2023, vol. 10, issue 6, No 10, 1607-1634

Abstract: Abstract Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework.

Keywords: Substance abuse; Individual vulnerability; Machine learning; Predictive analytics; Association rules; Risk factors decomposition (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-022-00381-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-022-00381-0

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-022-00381-0

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().